{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb09fc90-9f05-4183-b30b-6f662945a17f",
   "metadata": {},
   "source": [
    "# 1. Bring in full data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08ecb20-b8ef-4bad-aab4-0905c23a9c19",
   "metadata": {},
   "source": [
    "# 1.1 Basic imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62cd8922-6bb6-42a5-936a-7acce278343a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from basic_libraries import os, np, xr, pd, plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a23ac452-764c-4343-ba10-e2119d039edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ape_funcs import get_full_data_fpaths, surf_temp_giver, da_giver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb9008c2-f147-4503-889a-3ec9e2765647",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d7389b1-4f80-43bc-bfea-6455825005df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import first_part_story_funcs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ec7529-a71b-4f44-800e-8832c2f4e7b7",
   "metadata": {},
   "source": [
    "## 1.2 Open up precip. data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc0b50dc-3d62-4df9-b3a2-466f12aa91d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SAM | 295K] Loaded precipitation data.\n",
      "[SAM | 300K] Loaded precipitation data.\n",
      "[SAM | 305K] Loaded precipitation data.\n",
      "[CM1 | 295K] Loaded precipitation data.\n",
      "[CM1 | 300K] Loaded precipitation data.\n",
      "[CM1 | 305K] Loaded precipitation data.\n",
      "[SCALE | 295K] Loaded precipitation data.\n",
      "[SCALE | 300K] Loaded precipitation data.\n",
      "[SCALE | 305K] Loaded precipitation data.\n",
      "[UCLA-CRM | 295K] Loaded precipitation data.\n",
      "[UCLA-CRM | 300K] Loaded precipitation data.\n",
      "[UCLA-CRM | 305K] Loaded precipitation data.\n",
      "[DAM | 295K] Loaded precipitation data.\n",
      "[DAM | 300K] Loaded precipitation data.\n",
      "[DAM | 305K] Loaded precipitation data.\n",
      "[MESONH | 295K] Loaded precipitation data.\n",
      "[MESONH | 300K] Loaded precipitation data.\n",
      "[MESONH | 305K] Loaded precipitation data.\n",
      "[UKMOi-vn11.0-CASIM | 295K] Loaded precipitation data.\n",
      "[UKMOi-vn11.0-CASIM | 300K] Loaded precipitation data.\n",
      "[UKMOi-vn11.0-CASIM | 305K] Loaded precipitation data.\n",
      "[WRF_COL_CRM | 295K] Loaded precipitation data.\n",
      "[WRF_COL_CRM | 300K] Loaded precipitation data.\n",
      "[WRF_COL_CRM | 305K] Loaded precipitation data.\n"
     ]
    }
   ],
   "source": [
    "# specify base path to RCEMIP data\n",
    "base_path = r\"/...\"\n",
    "rcemip_Ts = [\"295\", \"300\", \"305\"]\n",
    "prec_var = \"pr\"\n",
    "\n",
    "# Model-specific metadata\n",
    "models = {\n",
    "    \"SAM\": {\n",
    "        \"subdir\": \"SAM/2D\",\n",
    "        \"prefix\": \"SAM_CRM_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"CM1\": {\n",
    "        \"subdir\": \"CM1/2D\",\n",
    "        \"prefix\": \"CM1_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"SCALE\": {\n",
    "        \"subdir\": \"SCALE/2D\",\n",
    "        \"prefix\": \"SCALE_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"UCLA-CRM\": {\n",
    "        \"subdir\": \"UCLA-CRM/2D\",\n",
    "        \"prefix\": \"UCLA-CRM_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"DAM\": {\n",
    "        \"subdir\": \"DAM/2D\",\n",
    "        \"prefix\": \"DAM_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"MESONH\": {\n",
    "        \"subdir\": \"MESONH/2D\",\n",
    "        \"prefix\": \"MESONH_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"UKMOi-vn11.0-CASIM\": {\n",
    "        \"subdir\": \"UKMOi-vn11.0-CASIM/2D\",\n",
    "        \"prefix\": \"UKMOi-vn11.0-CASIM_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    },\n",
    "    \"WRF_COL_CRM\": {\n",
    "        \"subdir\": \"WRF_COL_CRM/2D\",\n",
    "        \"prefix\": \"WRF_COL_CRM_RCE_small\",\n",
    "        \"suffix\": \"_2D_pr_last25d.nc\"\n",
    "    }\n",
    "}\n",
    "\n",
    "# Container for precipitation data\n",
    "prec_data = {}\n",
    "\n",
    "# Load precipitation data\n",
    "for model, meta in models.items():\n",
    "    model_path = os.path.join(base_path, meta[\"subdir\"])\n",
    "    prec_data[model] = {}\n",
    "    \n",
    "    temps = [t for t in rcemip_Ts if \"exclude\" not in meta or t not in meta[\"exclude\"]]\n",
    "    \n",
    "    for Ts in temps:\n",
    "        filename = f\"{meta['prefix']}{Ts}{meta['suffix']}\"\n",
    "        full_path = os.path.join(model_path, filename)\n",
    "        \n",
    "        if os.path.exists(full_path):\n",
    "            try:\n",
    "                ds = xr.open_dataset(full_path, decode_times=False)\n",
    "                prec_data[model][Ts] = ds[prec_var].values\n",
    "                print(f\"[{model} | {Ts}K] Loaded precipitation data.\")\n",
    "            except Exception as e:\n",
    "                print(f\"[{model} | {Ts}K] Failed to open: {e}\")\n",
    "        else:\n",
    "            print(f\"[{model} | {Ts}K] File not found: {full_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f02ff58f-27f0-4c39-bde9-48ebcc7bd120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SAM | 295K] Loaded 1D specific humidity data.\n",
      "[SAM | 300K] Loaded 1D specific humidity data.\n",
      "[SAM | 305K] Loaded 1D specific humidity data.\n",
      "[CM1 | 295K] Loaded 1D specific humidity data.\n",
      "[CM1 | 300K] Loaded 1D specific humidity data.\n",
      "[CM1 | 305K] Loaded 1D specific humidity data.\n",
      "[SCALE | 295K] Loaded 1D specific humidity data.\n",
      "[SCALE | 300K] Loaded 1D specific humidity data.\n",
      "[SCALE | 305K] Loaded 1D specific humidity data.\n",
      "[UCLA-CRM | 295K] Loaded 1D specific humidity data.\n",
      "[UCLA-CRM | 300K] Loaded 1D specific humidity data.\n",
      "[UCLA-CRM | 305K] Loaded 1D specific humidity data.\n",
      "[DAM | 295K] Loaded 1D specific humidity data.\n",
      "[DAM | 300K] Loaded 1D specific humidity data.\n",
      "[DAM | 305K] Loaded 1D specific humidity data.\n",
      "[MESONH | 295K] Loaded 1D specific humidity data.\n",
      "[MESONH | 300K] Loaded 1D specific humidity data.\n",
      "[MESONH | 305K] Loaded 1D specific humidity data.\n",
      "[UKMOi-vn11.0-CASIM | 295K] Loaded 1D specific humidity data.\n",
      "[UKMOi-vn11.0-CASIM | 300K] Loaded 1D specific humidity data.\n",
      "[UKMOi-vn11.0-CASIM | 305K] Loaded 1D specific humidity data.\n",
      "[WRF_COL_CRM | 295K] Loaded 1D specific humidity data.\n",
      "[WRF_COL_CRM | 300K] Loaded 1D specific humidity data.\n",
      "[WRF_COL_CRM | 305K] Loaded 1D specific humidity data.\n"
     ]
    }
   ],
   "source": [
    "#root specific humidty directory\n",
    "root_qv_dir = r'...'\n",
    "\n",
    "# Variable name for temperature\n",
    "qv_var = \"hus\"\n",
    "\n",
    "# Temperature data dictionary\n",
    "qv_data = {}\n",
    "\n",
    "# Model-specific metadata for ta files\n",
    "qv_models = {\n",
    "    \"SAM\": {\n",
    "        \"subdir\": \"SAM/1D\",\n",
    "        \"prefix\": \"SAM_CRM_\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    },\n",
    "    \"CM1\": {\n",
    "        \"subdir\": \"CM1/1D\",\n",
    "        \"prefix\": \"CM1_\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    },\n",
    "    \"SCALE\": {\n",
    "        \"subdir\": \"SCALE/1D\",\n",
    "        \"prefix\": \"SCALE_\",\n",
    "        \"suffix\": \"_hus_vertprof_full.nc\"\n",
    "    },\n",
    "    \"UCLA-CRM\": {\n",
    "        \"subdir\": \"UCLA-CRM/1D\",\n",
    "        \"prefix\": \"UCLA-CRM_\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    },\n",
    "    \"DAM\": {\n",
    "        \"subdir\": \"DAM/1D\",\n",
    "        \"prefix\": \"DAM_\",\n",
    "        \"suffix\": \"_hus_vertprof.nc\"\n",
    "    },\n",
    "    \"MESONH\": {\n",
    "        \"subdir\": \"MESONH/1D\",\n",
    "        \"prefix\": \"MESONH_\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    },\n",
    "    \"UKMOi-vn11.0-CASIM\": {\n",
    "        \"subdir\": \"UKMO/1D\",\n",
    "        \"prefix\": \"UKMOi_RCE_small\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    },\n",
    "    \"WRF_COL_CRM\": {\n",
    "        \"subdir\": \"WRF_COL_CRM/1D\",\n",
    "        \"prefix\": \"WRF_COL_CRM_\",\n",
    "        \"suffix\": \"_hus_vertprof_last25d.nc\"\n",
    "    }\n",
    "}\n",
    "\n",
    "# Loop to read temperature data\n",
    "for model, meta in qv_models.items():\n",
    "    model_path = os.path.join(root_qv_dir, meta[\"subdir\"])\n",
    "    qv_data[model] = {}\n",
    "\n",
    "    temps = [t for t in rcemip_Ts if \"exclude\" not in meta or t not in meta[\"exclude\"]]\n",
    "\n",
    "    for Ts in temps:\n",
    "        filename = f\"{meta['prefix']}{Ts}{meta['suffix']}\"\n",
    "        full_path = os.path.join(model_path, filename)\n",
    "\n",
    "        if os.path.exists(full_path):\n",
    "            try:\n",
    "                ds = xr.open_dataset(full_path, decode_times=False)\n",
    "                qv_data[model][Ts] = ds[qv_var]\n",
    "                print(f\"[{model} | {Ts}K] Loaded 1D specific humidity data.\")\n",
    "            except Exception as e:\n",
    "                print(f\"[{model} | {Ts}K] Failed to open: {e}\")\n",
    "        else:\n",
    "            print(f\"[{model} | {Ts}K] File not found: {full_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f786c586-3c67-4313-bd5b-73f8151f8462",
   "metadata": {},
   "source": [
    "# 2. Make distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5236037a-1ac5-45a7-8fd4-b16d2f0b7d6e",
   "metadata": {},
   "source": [
    "## 2.1 Import relevant functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e51a2c8-c224-432e-841f-33b7f227d52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep cell in case you want to see the log-structure plots\n",
    "from freq_amount_funcs import calculate_log_ratios\n",
    "from freq_amount_funcs import calc_Fd_and_histNormCounts as calc_dists\n",
    "from freq_amount_funcs import process_surface_temperature_cases as pstc \n",
    "from freq_amount_funcs import geo_series, midpoint_giver\n",
    "\n",
    "\n",
    "boundaries = geo_series(1.15,0.001,2000) #boundaries of the distributions at 7%, moe means \"midpoint of edges\"\n",
    "\n",
    "moe = midpoint_giver(boundaries)\n",
    "delLogR = calculate_log_ratios(boundaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38c8ea64-167f-4e53-9bc5-6ca86eab25d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert precipitation units from kg/m²/s to mm/day\n",
    "def convert_prec_units_to_mmday(prec_data):\n",
    "    conversion_factor = 24 * 3600  # seconds per day\n",
    "    converted_prec_data = {}\n",
    "\n",
    "    for model, temp_dict in prec_data.items():\n",
    "        converted_prec_data[model] = {}\n",
    "\n",
    "        for Ts, arr in temp_dict.items():\n",
    "            converted_prec_data[model][Ts] = arr * conversion_factor\n",
    "\n",
    "    return converted_prec_data\n",
    "\n",
    "prec_data_mmPday = convert_prec_units_to_mmday(prec_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f3f1afd-e5e7-44ed-9eb3-1ac07afad168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 108, 108)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_data_mmPday['DAM']['305'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1a1039-13f6-4aeb-b9c8-306c637a8525",
   "metadata": {},
   "source": [
    "### 2.1.1 Set up to run process surf temp cases (pstc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aaae9fe-7ea6-4f57-a290-6dc98fecbf3a",
   "metadata": {},
   "source": [
    "## 2.2 Run your function to get amount distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c08fe0c-6164-4fde-99e5-1d777abd90c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SAM] Processed successfully.\n",
      "[CM1] Processed successfully.\n",
      "[SCALE] Processed successfully.\n",
      "[UCLA-CRM] Processed successfully.\n",
      "[DAM] Processed successfully.\n",
      "[MESONH] Processed successfully.\n",
      "[UKMOi-vn11.0-CASIM] Processed successfully.\n",
      "[WRF_COL_CRM] Processed successfully.\n"
     ]
    }
   ],
   "source": [
    "#s_labels = [0.1, 1, 10, 100, 1000, 3000]\n",
    "sfig = r'/Volumes/COO/MFP_NJ/RESULTS/FIGS/physics_based_inc_shft'\n",
    "\n",
    "\n",
    "from freq_amount_funcs import (\n",
    "    process_surface_temperature_cases as pstc,\n",
    "    calc_Fd_and_histNormCounts as calc_Fd_n_Hist\n",
    ")\n",
    "\n",
    "# Initialize output dictionary\n",
    "dist_dic = {}\n",
    "\n",
    "# Process each model's precipitation data\n",
    "for model, Ts_dict in prec_data_mmPday.items(): # change dictionary here to process\n",
    "    # Skip models with fewer than two temperature states\n",
    "    if len(Ts_dict) < 2:\n",
    "        print(f\"[{model}] Skipped: not enough temperature states.\")\n",
    "        continue\n",
    "\n",
    "    # Sort temperature keys numerically\n",
    "    available_Ts = sorted(list(prec_data_mmPday[model].keys()))\n",
    "    arrays = [Ts_dict[Ts] for Ts in available_Ts]\n",
    "\n",
    "    try:\n",
    "        # Call processing function\n",
    "        f_dists, a_dists = pstc(\n",
    "            sfig, arrays, available_Ts,\n",
    "            boundaries, boundaries[0], delLogR,\n",
    "            moe, calc_Fd_n_Hist,'notes_from_runs_RCEMIP_06_12_25',model\n",
    "        )\n",
    "\n",
    "        # Store results per temperature\n",
    "        dist_dic[model] = {\n",
    "            Ts: (f, a) for Ts, f, a in zip(available_Ts, f_dists, a_dists)\n",
    "        }\n",
    "\n",
    "        print(f\"[{model}] Processed successfully.\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"[{model}] Failed during processing: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab698a9-9c7c-4fbd-980c-565824eabdc7",
   "metadata": {},
   "source": [
    "### 2.2.1 Check areas of amount curves--compare to prec array means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7cbe9f31-555f-41c6-9df8-d50e8b974196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SAM': {'295': np.float64(2.221265595895001),\n",
       "  '300': np.float64(2.7416038477332743),\n",
       "  '305': np.float64(3.420813069441545)},\n",
       " 'CM1': {'295': np.float64(2.2596415489184594),\n",
       "  '300': np.float64(2.755295641124824),\n",
       "  '305': np.float64(3.4138530000099747)},\n",
       " 'SCALE': {'295': np.float64(2.2832935912516756),\n",
       "  '300': np.float64(2.7590175496476492),\n",
       "  '305': np.float64(3.461637386589076)},\n",
       " 'UCLA-CRM': {'295': np.float64(2.6103427344317263),\n",
       "  '300': np.float64(3.542914092174613),\n",
       "  '305': np.float64(4.387429278404715)},\n",
       " 'DAM': {'295': np.float64(2.085616849570389),\n",
       "  '300': np.float64(2.627638421496822),\n",
       "  '305': np.float64(3.340331268200152)},\n",
       " 'MESONH': {'295': np.float64(1.2914484664406172),\n",
       "  '300': np.float64(1.7036050767788253),\n",
       "  '305': np.float64(2.2465033233641813)},\n",
       " 'UKMOi-vn11.0-CASIM': {'295': np.float64(2.594566040151077),\n",
       "  '300': np.float64(3.0245774965488166),\n",
       "  '305': np.float64(3.3309174141876934)},\n",
       " 'WRF_COL_CRM': {'295': np.float64(2.313049476375065),\n",
       "  '300': np.float64(2.9006802304756576),\n",
       "  '305': np.float64(3.5199695601795735)}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check areas under amount distributions\n",
    "amount_areas = {}\n",
    "\n",
    "for model, ts_dict in dist_dic.items():\n",
    "    amount_areas[model] = {}\n",
    "\n",
    "    for Ts, (freq, amount) in ts_dict.items():\n",
    "        area = np.sum(amount * delLogR)\n",
    "        amount_areas[model][Ts] = area\n",
    "amount_areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83143617-5c52-4f73-9028-c5f79bc40d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SAM': {'295': np.float32(2.2191315),\n",
       "  '300': np.float32(2.738946),\n",
       "  '305': np.float32(3.4174483)},\n",
       " 'CM1': {'295': np.float32(2.2577517),\n",
       "  '300': np.float32(2.7532256),\n",
       "  '305': np.float32(3.4113035)},\n",
       " 'SCALE': {'295': np.float32(2.2814252),\n",
       "  '300': np.float32(2.756542),\n",
       "  '305': np.float32(3.4585218)},\n",
       " 'UCLA-CRM': {'295': np.float64(2.607911667033094),\n",
       "  '300': np.float64(3.5407072739947054),\n",
       "  '305': np.float64(4.384691885548847)},\n",
       " 'DAM': {'295': np.float64(2.0838748887919887),\n",
       "  '300': np.float64(2.625592795185008),\n",
       "  '305': np.float64(3.3378422458522885)},\n",
       " 'MESONH': {'295': np.float32(1.290282),\n",
       "  '300': np.float32(1.7022743),\n",
       "  '305': np.float32(2.2446291)},\n",
       " 'UKMOi-vn11.0-CASIM': {'295': np.float32(2.596109),\n",
       "  '300': np.float32(3.1867),\n",
       "  '305': np.float32(3.820552)},\n",
       " 'WRF_COL_CRM': {'295': np.float32(2.3112845),\n",
       "  '300': np.float32(2.8977773),\n",
       "  '305': np.float32(3.5166037)}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check mean of preciptation arrays\n",
    "prec_array_means = {}\n",
    "\n",
    "for model, ts_dict in prec_data_mmPday.items():\n",
    "    prec_array_means[model] = {}\n",
    "\n",
    "    for Ts, arreglo in ts_dict.items():\n",
    "        mean = np.mean(arreglo)\n",
    "        prec_array_means[model][Ts] = mean\n",
    "prec_array_means"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576bceda-aa94-4136-9871-fb0efceac39a",
   "metadata": {},
   "source": [
    "## 2.2.2 Get hydrological sensitivity of different integrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a92a2e9d-9cbf-4e8a-89ec-2777c7a40a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize output dictionary\n",
    "hydro_frac_change = {}\n",
    "\n",
    "# Compute hydrological sensitivity per model\n",
    "for model, Ts_dict in prec_data_mmPday.items():\n",
    "    mean_prec = {}\n",
    "\n",
    "    # Compute domain mean precipitation for each temperature\n",
    "    for Ts, arr in Ts_dict.items():\n",
    "        mean_prec[Ts + \"K\"] = np.mean(arr)\n",
    "\n",
    "    # Extract sensitivities\n",
    "    hs = {}\n",
    "\n",
    "    if \"295K\" in mean_prec and \"300K\" in mean_prec:\n",
    "        hs[\"295-300K\"] = np.log(mean_prec[\"300K\"] / mean_prec[\"295K\"])\n",
    "\n",
    "    if \"300K\" in mean_prec and \"305K\" in mean_prec:\n",
    "        hs[\"300-305K\"] = np.log(mean_prec[\"305K\"] / mean_prec[\"300K\"])\n",
    "\n",
    "    if \"295K\" in mean_prec and \"305K\" in mean_prec:\n",
    "        hs[\"295-305K\"] =  np.log(mean_prec[\"305K\"] / mean_prec[\"295K\"])\n",
    "\n",
    "    hydro_frac_change[model] = hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d23aebc6-aba8-4183-be4e-b4e72005d476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SAM': {'295-300K': np.float32(0.21045728),\n",
       "  '300-305K': np.float32(0.22132105),\n",
       "  '295-305K': np.float32(0.43177828)},\n",
       " 'CM1': {'295-300K': np.float32(0.19840366),\n",
       "  '300-305K': np.float32(0.21432132),\n",
       "  '295-305K': np.float32(0.412725)},\n",
       " 'SCALE': {'295-300K': np.float32(0.18917662),\n",
       "  '300-305K': np.float32(0.22686434),\n",
       "  '295-305K': np.float32(0.41604096)},\n",
       " 'UCLA-CRM': {'295-300K': np.float64(0.30577672861493893),\n",
       "  '300-305K': np.float64(0.21379285569902604),\n",
       "  '295-305K': np.float64(0.519569584313965)},\n",
       " 'DAM': {'295-300K': np.float64(0.2310776093656274),\n",
       "  '300-305K': np.float64(0.2400178667569358),\n",
       "  '295-305K': np.float64(0.4710954761225631)},\n",
       " 'MESONH': {'295-300K': np.float32(0.2771044),\n",
       "  '300-305K': np.float32(0.27657512),\n",
       "  '295-305K': np.float32(0.55367947)},\n",
       " 'UKMOi-vn11.0-CASIM': {'295-300K': np.float32(0.20497216),\n",
       "  '300-305K': np.float32(0.18140903),\n",
       "  '295-305K': np.float32(0.38638124)},\n",
       " 'WRF_COL_CRM': {'295-300K': np.float32(0.22614056),\n",
       "  '300-305K': np.float32(0.19355169),\n",
       "  '295-305K': np.float32(0.41969222)}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hydro_frac_change"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5002b3-d526-4f8f-8cf0-4cb4d9d0ba6b",
   "metadata": {},
   "source": [
    "# 2.3 Find the fractional change in specific humidity at the surface for new RCEMIP data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406ab355-b557-44c7-8a97-ddae7a66ee30",
   "metadata": {},
   "source": [
    "### 2.3.0 Visualize the models specific humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0bed922-b777-4996-85cf-0e4738f82348",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_qv_dm_profile = r'...'\n",
    "fig_name = '....png'\n",
    "sdir1 = os.path.join(save_qv_dm_profile,fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "351a96ed-01a9-4b2a-88c1-f2982591782d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "models = list(qv_data.keys())\n",
    "cases = ['295', '300', '305']\n",
    "\n",
    "fig, axs = plt.subplots(1, len(models), figsize=(22, 5), sharey=False, sharex=True)\n",
    "\n",
    "for i, model in enumerate(models):\n",
    "    ax = axs[i]\n",
    "    for case in cases:\n",
    "        da = qv_data[model][case]\n",
    "        vertical_dim = da.dims[0]\n",
    "        vertical_coord = da.coords[vertical_dim]\n",
    "        ax.plot(da.values[:10], vertical_coord.values[:10], label=f\"{case} K\")\n",
    "\n",
    "    ax.set_title(model, fontsize=10)\n",
    "    ax.set_xlabel(\"qv [g/g]\")\n",
    "    ax.grid(True)\n",
    "\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(f\"Various vertical units\")\n",
    "    if model == 'WRF_COL_CRM':\n",
    "        ax.invert_yaxis()\n",
    "\n",
    "fig.suptitle(\"Domain-Mean Specific Humidity Profiles by Model\", fontsize=14)\n",
    "axs[-1].legend(title=\"Surface Temp\", loc='upper right')\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "plt.savefig(sdir1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "19a17431-11ce-4688-adb4-f4b60025f51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cases = ['295', '300', '305']\n",
    "z_n = qv_data['MESONH']['295'].dims[0] # shared height coordinate\n",
    "z = qv_data['MESONH']['295'].coords[z_n]\n",
    "\n",
    "plt.figure(figsize=(6, 8))\n",
    "for case in cases:\n",
    "    qv = qv_data['MESONH'][case].values[:5]\n",
    "    plt.plot(qv, z[:5], label=f\"{case} K\")\n",
    "\n",
    "plt.xlabel(\"Specific Humidity [g/g]\")\n",
    "plt.ylabel(\"Vertical units\")\n",
    "plt.title(\"Vertical Profile of Specific Humidity\")\n",
    "plt.grid(True)\n",
    "plt.legend(title=\"Surface Temp\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.savefig(sdir1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38015eff-3202-42b3-a35c-c71f77660da0",
   "metadata": {},
   "source": [
    "### 2.3.1 Functions to calculate the fractional change in specific humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04798365-d60f-4f45-9616-986c4fbcd39d",
   "metadata": {},
   "outputs": [],
   "source": [
    "qv_second_values = {}\n",
    "\n",
    "for model in qv_data.keys():\n",
    "    qv_second_values[model] = {}\n",
    "    for case in ['295', '300', '305']:\n",
    "        da = qv_data[model][case]\n",
    "        values = da.values\n",
    "        val = values[1]\n",
    "       \n",
    "        qv_second_values[model][case] = float(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d060b98-5207-4cc0-bb82-3429883f87f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'295': 0.000902496394701302,\n",
       " '300': 0.0009287125431001186,\n",
       " '305': 0.0009480150183662772}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qv_second_values['MESONH']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35214e98-302a-460c-8c3c-d346a0851891",
   "metadata": {},
   "source": [
    "### 2.3.2 calculate actual fractional change in surface specific humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee008a23-b5bb-44be-b9a4-e0f206703178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SAM': {'295-300K': np.float64(0.3591541664662393),\n",
       "  '300-305K': np.float64(0.34158108833925543),\n",
       "  '295-305K': np.float64(0.7007352548054949)},\n",
       " 'CM1': {'295-300K': np.float64(0.36791271641741524),\n",
       "  '300-305K': np.float64(0.3408531679559657),\n",
       "  '295-305K': np.float64(0.7087658843733807)},\n",
       " 'SCALE': {'295-300K': np.float64(0.37102045189295046),\n",
       "  '300-305K': np.float64(0.3416818585351877),\n",
       "  '295-305K': np.float64(0.7127023104281381)},\n",
       " 'UCLA-CRM': {'295-300K': np.float64(0.35794736395969545),\n",
       "  '300-305K': np.float64(0.34411696462821345),\n",
       "  '295-305K': np.float64(0.7020643285879089)},\n",
       " 'DAM': {'295-300K': np.float64(0.3348124885495246),\n",
       "  '300-305K': np.float64(0.3212798239761247),\n",
       "  '295-305K': np.float64(0.6560923125256494)},\n",
       " 'MESONH': {'295-300K': np.float64(0.028634569366401404),\n",
       "  '300-305K': np.float64(0.020571079513399416),\n",
       "  '295-305K': np.float64(0.049205648879800654)},\n",
       " 'UKMOi-vn11.0-CASIM': {'295-300K': np.float64(0.2995116553387788),\n",
       "  '300-305K': np.float64(0.3327255530335486),\n",
       "  '295-305K': np.float64(0.6322372083723273)},\n",
       " 'WRF_COL_CRM': {'295-300K': np.float64(0.3259313614718607),\n",
       "  '300-305K': np.float64(0.3267433040425429),\n",
       "  '295-305K': np.float64(0.6526746655144038)}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize dictionary to store fractional change results\n",
    "frac_change_qv = {}\n",
    "\n",
    "# Compute log differences per model\n",
    "for model, Ts_dict in qv_second_values.items():\n",
    "    model_result = {}\n",
    "\n",
    "    if \"295\" in Ts_dict and \"300\" in Ts_dict:\n",
    "        q295 = Ts_dict[\"295\"]\n",
    "        q300 = Ts_dict[\"300\"]\n",
    "        model_result[\"295-300K\"] = np.log(q300 / q295)\n",
    "\n",
    "    if \"300\" in Ts_dict and \"305\" in Ts_dict:\n",
    "        q300 = Ts_dict[\"300\"]\n",
    "        q305 = Ts_dict[\"305\"]\n",
    "        model_result[\"300-305K\"] = np.log(q305 / q300)\n",
    "\n",
    "    if \"295\" in Ts_dict and \"305\" in Ts_dict:\n",
    "        q295 = Ts_dict[\"295\"]\n",
    "        q305 = Ts_dict[\"305\"]\n",
    "        model_result[\"295-305K\"] = np.log(q305 / q295)\n",
    "\n",
    "    frac_change_qv[model] = model_result\n",
    "\n",
    "frac_change_qv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9abf49f6-2854-4aec-9b0b-1f04b7ae0d12",
   "metadata": {},
   "source": [
    "### 2.3.3 Save results into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3faea46-228b-4b9e-94db-d182c7cec85a",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_df_dir_hourly = r'...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b2bf95c-4e88-4975-8f16-d12592bc7dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join(...,'FIG2_df.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "149eafde-4332-4279-af9e-413eaa428e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>model</th>\n",
       "      <th>dellnP_295-300K</th>\n",
       "      <th>dellnP_300-305K</th>\n",
       "      <th>dellnP_295-305K</th>\n",
       "      <th>dellnqv_295-300K</th>\n",
       "      <th>dellnqv_300-305K</th>\n",
       "      <th>dellnqv_295-305K</th>\n",
       "      <th>delta_295_300K</th>\n",
       "      <th>delta_300_305K</th>\n",
       "      <th>delta_295_305K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SCALE</td>\n",
       "      <td>0.189177</td>\n",
       "      <td>0.226864</td>\n",
       "      <td>0.416041</td>\n",
       "      <td>0.371020</td>\n",
       "      <td>0.341682</td>\n",
       "      <td>0.712702</td>\n",
       "      <td>0.181844</td>\n",
       "      <td>0.114818</td>\n",
       "      <td>0.296661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WRF_COL_CRM</td>\n",
       "      <td>0.226141</td>\n",
       "      <td>0.193552</td>\n",
       "      <td>0.419692</td>\n",
       "      <td>0.325931</td>\n",
       "      <td>0.326743</td>\n",
       "      <td>0.652675</td>\n",
       "      <td>0.099791</td>\n",
       "      <td>0.133192</td>\n",
       "      <td>0.232982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>UKMOi-vn11.0-CASIM</td>\n",
       "      <td>0.204972</td>\n",
       "      <td>0.181409</td>\n",
       "      <td>0.386381</td>\n",
       "      <td>0.299512</td>\n",
       "      <td>0.332726</td>\n",
       "      <td>0.632237</td>\n",
       "      <td>0.094539</td>\n",
       "      <td>0.151317</td>\n",
       "      <td>0.245856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>MESONH</td>\n",
       "      <td>0.277105</td>\n",
       "      <td>0.276574</td>\n",
       "      <td>0.553680</td>\n",
       "      <td>0.302410</td>\n",
       "      <td>0.311785</td>\n",
       "      <td>0.614195</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>0.035210</td>\n",
       "      <td>0.060515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>SAM</td>\n",
       "      <td>0.210457</td>\n",
       "      <td>0.221321</td>\n",
       "      <td>0.431778</td>\n",
       "      <td>0.359154</td>\n",
       "      <td>0.341581</td>\n",
       "      <td>0.700735</td>\n",
       "      <td>0.148697</td>\n",
       "      <td>0.120260</td>\n",
       "      <td>0.268957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>UCLA-CRM</td>\n",
       "      <td>0.305777</td>\n",
       "      <td>0.213793</td>\n",
       "      <td>0.519570</td>\n",
       "      <td>0.357947</td>\n",
       "      <td>0.344117</td>\n",
       "      <td>0.702064</td>\n",
       "      <td>0.052171</td>\n",
       "      <td>0.130324</td>\n",
       "      <td>0.182495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>DAM</td>\n",
       "      <td>0.231078</td>\n",
       "      <td>0.240018</td>\n",
       "      <td>0.471095</td>\n",
       "      <td>0.334812</td>\n",
       "      <td>0.321280</td>\n",
       "      <td>0.656092</td>\n",
       "      <td>0.103735</td>\n",
       "      <td>0.081262</td>\n",
       "      <td>0.184997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>CM1</td>\n",
       "      <td>0.198404</td>\n",
       "      <td>0.214321</td>\n",
       "      <td>0.412725</td>\n",
       "      <td>0.367913</td>\n",
       "      <td>0.340853</td>\n",
       "      <td>0.708766</td>\n",
       "      <td>0.169509</td>\n",
       "      <td>0.126532</td>\n",
       "      <td>0.296041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0               model  dellnP_295-300K  \\\n",
       "0             0           0               SCALE         0.189177   \n",
       "1             1           1         WRF_COL_CRM         0.226141   \n",
       "2             2           2  UKMOi-vn11.0-CASIM         0.204972   \n",
       "3             3           4              MESONH         0.277105   \n",
       "4             4           4                 SAM         0.210457   \n",
       "5             5           5            UCLA-CRM         0.305777   \n",
       "6             6           6                 DAM         0.231078   \n",
       "7             7           7                 CM1         0.198404   \n",
       "\n",
       "   dellnP_300-305K  dellnP_295-305K  dellnqv_295-300K  dellnqv_300-305K  \\\n",
       "0         0.226864         0.416041          0.371020          0.341682   \n",
       "1         0.193552         0.419692          0.325931          0.326743   \n",
       "2         0.181409         0.386381          0.299512          0.332726   \n",
       "3         0.276574         0.553680          0.302410          0.311785   \n",
       "4         0.221321         0.431778          0.359154          0.341581   \n",
       "5         0.213793         0.519570          0.357947          0.344117   \n",
       "6         0.240018         0.471095          0.334812          0.321280   \n",
       "7         0.214321         0.412725          0.367913          0.340853   \n",
       "\n",
       "   dellnqv_295-305K  delta_295_300K  delta_300_305K  delta_295_305K  \n",
       "0          0.712702        0.181844        0.114818        0.296661  \n",
       "1          0.652675        0.099791        0.133192        0.232982  \n",
       "2          0.632237        0.094539        0.151317        0.245856  \n",
       "3          0.614195        0.025305        0.035210        0.060515  \n",
       "4          0.700735        0.148697        0.120260        0.268957  \n",
       "5          0.702064        0.052171        0.130324        0.182495  \n",
       "6          0.656092        0.103735        0.081262        0.184997  \n",
       "7          0.708766        0.169509        0.126532        0.296041  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6b7647-7d89-467f-83e5-72fb0ba9024f",
   "metadata": {},
   "source": [
    "# 3. Make Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c7426e-8869-4555-a97c-0eedd89293b7",
   "metadata": {},
   "source": [
    "### 3.1 Define functions for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "53f3f842-5d1f-404d-b8d1-7f965ef97575",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define colorblind-friendly colors for temperature cases\n",
    "colors = ['#377eb8', '#4daf4a', '#984ea3', '#e41a1c']  # Blue to red\n",
    "#selected_labels = [0.1, 1, 10, 100, 1000, 3000]\n",
    "from first_part_story_funcs import (panel_stair, plot_dual_row_transition_panels, plot_2x4_transition_panels,plot_4x4_transition_panels_with_insets,\n",
    "    compute_distribution_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "be4f08ac-e6d4-437f-bc9e-c16cca913ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['295', '300', '305'])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_dic['SAM'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "f18b823a-0b76-489b-9818-e8fe130bc127",
   "metadata": {},
   "outputs": [],
   "source": [
    "sfig = r'...'\n",
    "figname = '...'\n",
    "save_fig = os.path.join(sfig, figname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "89395d48-cc27-44cf-8b55-dc3f7cdaeaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_dict = {\n",
    "    \"CM1\": {\n",
    "        \"295-305K\": 0.20,\n",
    "        \"295-300K\": 0.17,\n",
    "        \"300-305K\": 0.052,\n",
    "    },\n",
    "    \"DAM\": {\n",
    "        \"295-305K\": 0.14,\n",
    "        \"295-300K\": 0.07,\n",
    "        \"300-305K\": 0.07,\n",
    "    },\n",
    "    \"MESONH\": {\n",
    "        \"295-305K\": 0.32,\n",
    "        \"295-300K\": 0.17,\n",
    "        \"300-305K\": 0.16,\n",
    "    },\n",
    "    \"SAM\": {\n",
    "        \"295-305K\": 0.05,\n",
    "        \"295-300K\": 0.04,\n",
    "        \"300-305K\": 0.05,\n",
    "    },\n",
    "    \"SCALE\": {\n",
    "        \"295-305K\": 0.24,\n",
    "        \"295-300K\": 0.19,\n",
    "        \"300-305K\": 0.07,\n",
    "    },\n",
    "    \"UCLA-CRM\": {\n",
    "        \"295-305K\": 0.10,\n",
    "        \"295-300K\": 0.04,\n",
    "        \"300-305K\": 0.09,\n",
    "    },\n",
    "    \"UKMOi-vn11.0-CASIM\": {\n",
    "        \"295-305K\": 0.13,\n",
    "        \"295-300K\": 0.17,\n",
    "        \"300-305K\": 0.04,\n",
    "    },\n",
    "    \"WRF_COL_CRM\": {\n",
    "        \"295-305K\": 0.21,\n",
    "        \"295-300K\": 0.14,\n",
    "        \"300-305K\": 0.09,\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a5f5efd0-d354-4fbc-aa93-fa36c763b70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 670x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colores = ['crimson', 'navy','purple']\n",
    "\n",
    "plot_2x4_transition_panels(dist_dic=dist_dic,dist_type = \"frequency\",df=df, boundaries=boundaries,\n",
    "                                delLogR=delLogR,selected_labels= [0.1, 1, 10, 100, 1000, 2500],\n",
    "                                colors = colores, xlabel='mm/day', ylabel_freq = r\"Prob. / $\\Delta \\log(\\mathrm{rain\\ rate})$\",\n",
    "                                ylabel_amt=r\"Prob. / $\\Delta \\log(\\mathrm{rain\\ rate}) * rr$\",legend_pos = \"upper left\",\n",
    "                                comparison_mode=\"model\", fixed_transition=(\"295\",\"305\"),models = list(dist_dic.keys()),\n",
    "                                save_path=save_fig, errors_dict=errors_dict,save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b6eff25b-1ddc-42bb-9845-827d48b31f8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n",
      "295-300K\n",
      "300-305K\n",
      "295-305K\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'SAM': {'295-300K': np.float64(0.03704161791751063),\n",
       "  '300-305K': np.float64(0.05423118715167401),\n",
       "  '295-305K': np.float64(0.05257585101525811)},\n",
       " 'CM1': {'295-300K': np.float64(0.17254595511711296),\n",
       "  '300-305K': np.float64(0.05167144923822062),\n",
       "  '295-305K': np.float64(0.1975691412007057)},\n",
       " 'SCALE': {'295-300K': np.float64(0.19031127879665455),\n",
       "  '300-305K': np.float64(0.06907988666488811),\n",
       "  '295-305K': np.float64(0.24481568814901414)},\n",
       " 'UCLA-CRM': {'295-300K': np.float64(0.03782330811043287),\n",
       "  '300-305K': np.float64(0.08677659990013618),\n",
       "  '295-305K': np.float64(0.09979741418283522)},\n",
       " 'DAM': {'295-300K': np.float64(0.06778055259121912),\n",
       "  '300-305K': np.float64(0.06534763678890822),\n",
       "  '295-305K': np.float64(0.1367762951581296)},\n",
       " 'MESONH': {'295-300K': np.float64(0.1700780040656721),\n",
       "  '300-305K': np.float64(0.15743475718117972),\n",
       "  '295-305K': np.float64(0.32458272381300685)},\n",
       " 'UKMOi-vn11.0-CASIM': {'295-300K': np.float64(0.1710652422187282),\n",
       "  '300-305K': np.float64(0.039314352037491),\n",
       "  '295-305K': np.float64(0.13408307875447945)},\n",
       " 'WRF_COL_CRM': {'295-300K': np.float64(0.14303272625578095),\n",
       "  '300-305K': np.float64(0.08231276907764393),\n",
       "  '295-305K': np.float64(0.2122371148103305)}}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#L1 error: what fraction of the actual rainfall is not captured or misplaced by the model's transformation?\n",
    "error_dic = compute_distribution_errors(dist_dic,df,boundaries, list(dist_dic.keys()), [(\"295\", \"300\"), (\"300\", \"305\"), (\"295\", \"305\")])\n",
    "error_dic                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a969b5ea-02ec-4eb4-a30e-d8549812115f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>CM1</th>\n",
       "      <th>DAM</th>\n",
       "      <th>MESONH</th>\n",
       "      <th>SAM</th>\n",
       "      <th>SCALE</th>\n",
       "      <th>UCLA-CRM</th>\n",
       "      <th>UKMOi-vn11.0-CASIM</th>\n",
       "      <th>WRF_COL_CRM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295-300K</th>\n",
       "      <td>0.172546</td>\n",
       "      <td>0.067781</td>\n",
       "      <td>0.170078</td>\n",
       "      <td>0.037042</td>\n",
       "      <td>0.190311</td>\n",
       "      <td>0.037823</td>\n",
       "      <td>0.171065</td>\n",
       "      <td>0.143033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295-305K</th>\n",
       "      <td>0.197569</td>\n",
       "      <td>0.136776</td>\n",
       "      <td>0.324583</td>\n",
       "      <td>0.052576</td>\n",
       "      <td>0.244816</td>\n",
       "      <td>0.099797</td>\n",
       "      <td>0.134083</td>\n",
       "      <td>0.212237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300-305K</th>\n",
       "      <td>0.051671</td>\n",
       "      <td>0.065348</td>\n",
       "      <td>0.157435</td>\n",
       "      <td>0.054231</td>\n",
       "      <td>0.069080</td>\n",
       "      <td>0.086777</td>\n",
       "      <td>0.039314</td>\n",
       "      <td>0.082313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model            CM1       DAM    MESONH       SAM     SCALE  UCLA-CRM  \\\n",
       "transition                                                               \n",
       "295-300K    0.172546  0.067781  0.170078  0.037042  0.190311  0.037823   \n",
       "295-305K    0.197569  0.136776  0.324583  0.052576  0.244816  0.099797   \n",
       "300-305K    0.051671  0.065348  0.157435  0.054231  0.069080  0.086777   \n",
       "\n",
       "model       UKMOi-vn11.0-CASIM  WRF_COL_CRM  \n",
       "transition                                   \n",
       "295-300K              0.171065     0.143033  \n",
       "295-305K              0.134083     0.212237  \n",
       "300-305K              0.039314     0.082313  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def error_dict_to_dataframe(error_dic):\n",
    "    \"\"\"\n",
    "    Convert nested error dictionary to a DataFrame with:\n",
    "        - Rows = transitions (e.g., \"295-305K\")\n",
    "        - Columns = model names\n",
    "        - Values = relative L1 errors\n",
    "\n",
    "    Parameters:\n",
    "        error_dic (dict): Nested dictionary of the form:\n",
    "            {model_name: {transition_key: error_value}}\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: Pivoted DataFrame with transition as index and model as columns\n",
    "    \"\"\"\n",
    "    # Step 1: Flatten the nested dictionary\n",
    "    records = []\n",
    "    for model, transitions in error_dic.items():\n",
    "        for transition, value in transitions.items():\n",
    "            records.append((transition, model, float(value)))\n",
    "\n",
    "    # Step 2: Convert to DataFrame\n",
    "    df = pd.DataFrame(records, columns=[\"transition\", \"model\", \"error\"])\n",
    "\n",
    "    # Step 3: Pivot to get desired shape\n",
    "    pivot_df = df.pivot(index=\"transition\", columns=\"model\", values=\"error\")\n",
    "\n",
    "    return pivot_df\n",
    "\n",
    "error_df = error_dict_to_dataframe(error_dic)\n",
    "error_df"
   ]
  }
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